ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKS A comprehensive presentation of the implementation of artificial intelligence and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more Discussions of explainable neural networks and XAI Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology Perfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, along with policymakers and regulators who work in technology.

Adaptive WCDMA covers the latest theoretical principals and practice of wideband code division multiple access (WCDMA), which is the basis for the physical layer of the third generation mobile communications systems. The focus of this volume is on implementation losses characterising the system degradation due to imperfect implementation and in turn how much of the performance promised by theory should be expected in practical solutions based on a given technology, which is not perfect but has finite cost and power consumption. It also provides a significant amount of material related to the further developments and improvements (beyond 3G) especially sections on adaptive WCDMA and modifications for implementations in ad hoc networks.

Based on the author’s experience in research and teaching courses in the field at both university and industry, this volume provides a simultaneous account of the theoretical principles of the technology and how and why these principles are used in the standards.

Written in an accessible and simple to read style the book serves as both an introductory level text and as a more advanced reference. It has a step by step approach which introduces the theory and the standard to those new to the topic yet will meet the expectations of readers from varying backgrounds and levels.

Topics covered include:

• Fundamentals: provides an initial explanation about the principles of WCDMA
• Power Control: including algorithms and performance analysis with a comprehensive literature review in the field of adaptive systems
• Chapters on Code Acquisition and tracking, channel estimation, multi-user detection: provide a comprehensive description of algorithms and tools for their analysis
• CDMA Network Design: discusses network capacity and employment issues together with coverage
• Adaptive CDMA Networks: covers adaptive algorithms for maximizing throughput in the network